21 results about "Drugs types" patented technology

The embodiment of the invention provides a medicine evaluation method and related equipment, and belongs to the technical field of information processing. The method comprises the following steps: acquiring medicine input information corresponding to a first medicine; the first medicine is any medicine, and the medicine input information comprises medicine type data; generating a corresponding medicine evaluation template according to medicine type data in the medicine input information; collecting expert evaluation information corresponding to the first drug through the drug evaluation template; the expert evaluation information comprises text data and score data; and performing text analysis on the text data corresponding to the first drug through the drug evaluation model to generate a model evaluation score, and integrating the model evaluation score and the score data to obtain a target evaluation result corresponding to the first drug. According to the embodiment of the invention, the consistency and objectivity of the drug evaluation result can be remarkably improved, so that more comprehensive and intelligent decision support is provided for drug selection of medical institutions.

The invention relates to the technical field of medical evaluation, in particular to a basic drug use tendency evaluation method and device, a medium and a product. The method comprises the following steps: acquiring a disease type, a patient specific attribute and medication information of a target patient; according to a standard evidence-based intensity database, determining intermediate evidence-based intensity of each drug type in the medication information in disease types, patient specific attributes and medication schemes; obtaining attribute information of a target hospital seeing a doctor by the target patient, and adjusting the intermediate evidence-based intensity based on the attribute information to obtain the actual evidence-based intensity of the target patient; determining the average standard evidence-based intensity of each base drug type corresponding to the disease type; and calculating a difference value between the actual evidence-based intensity and the average standard evidence-based intensity as a base drug use tendency degree, and generating a drug use rationality evaluation result for the target patient according to the base drug use tendency degree. According to the invention, multi-dimensional evaluation can be carried out on the drug use rationality of the patient, and the accuracy of the evaluation result is improved.

This invention relates to the field of medical data and discloses a method for real-time monitoring of the efficacy of traditional Chinese medicine, Mongolian medicine, and Western medicine based on biosensor technology. This method enables real-time monitoring of drug efficacy and personalized medication guidance. The process includes collecting real-time multimodal biosignals from the user's body surface or minimally invasive body fluid samples to obtain raw sensor signals and acquire sensor array output; extracting real-time pharmacokinetic characteristic signals based on a preset pharmacokinetic model and mapping them to efficacy assessment parameters according to drug type; comparing the efficacy assessment parameters with a preset safe and effective concentration range to generate real-time medication status indicators and dosage adjustment prompts. This invention designs quantitative assessment methods for different types of drugs, enabling real-time comparison to generate efficacy status codes and dosage adjustment suggestions. It comprehensively generates a medication guidance report and transmits it in real-time to the user or clinical monitoring end, allowing for immediate adjustment of the medication regimen and ensuring safe and effective medication.

The present application relates to the technical field of artificial intelligence, and particularly relates to an individualized medication prediction method based on artificial intelligence, comprising: acquiring a plurality of standard historical medical data sets; acquiring metabolic capacity scores and drug metabolism rates corresponding to all data groups; training to obtain relationship models corresponding to all standard historical medical data sets; acquiring liver metabolism parameters, gene expression parameters and each drug name in a treatment scheme of a patient to obtain a drug metabolism rate; and determining a corresponding prediction drug amount determination method according to whether the drug types in the treatment scheme have a competitive relationship. The present application integrates drug metabolism enzyme-related gene expression and liver metabolism function, simultaneously incorporates drug absorption competition and metabolism enzyme competition when drugs are used in combination, corrects the final output prediction drug amount when drugs are used in combination, so that the model is more suitable for the actual drug use scene, and the accuracy of the prediction result is increased.

The application discloses an electrically-controlled drug-loaded hot compress patch, a control circuit and a control method. The control circuit comprises a drug-loaded hot compress patch and a controller. The drug-loaded hot compress patch and the controller are connected. The drug-loaded hot compress patch comprises a drug-loaded layer, a heating layer and a fixing belt. The heating layer is arranged on the drug-loaded layer. The fixing belt is arranged on the heating layer. The drug type of the drug-loaded layer is selected based on user demand. The drug-loaded layer and the heating layer are detachably connected. A plurality of electric heating patches are arranged on the heating layer. The heating layer is used for heating the drug-loaded layer. The fixing belt is used for fixing the drug-loaded layer to the human skin. The controller controls the electric heating patches in the heating layer based on the effective release temperature of the drug on the drug-loaded layer.

The invention discloses a composition, a method and a system for predicting the applicability of antipsychotic drugs. The invention develops a composition, a detection kit, a model construction method and a system for predicting the applicability of antipsychotic drugs on the basis of selected SNP site information. Particularly, a gene marker combination related to the curative effects of the antipsychotic drugs olanzapine, risperidone, aripiprazole and quetiapine is found by adopting a large-sample high-throughput whole-genome association analysis technology, and a corresponding mathematical model for predicting the curative effects is constructed; the model can be used for predicting the PANSS subtraction rate after the schizophrenia is treated by medication based on DNA polymorphic sites and clinical basic information of a medication user in a high, medium and low classification manner, and assists in guiding the selection of the types of the medication, so that personalized treatment of the schizophrenia patient is realized.

This invention discloses an automated dispensing device for injectable drugs, belonging to the field of medical device automation technology. The device includes a pluggable drug storage compartment and a dispensing base. A bottle guide ramp and a conical multi-position screw feeder work together to achieve self-alignment and precise bottle pickup. A dynamic locking mechanism automatically locks / unlocks the transmission system during insertion and removal to ensure safety. An RFID system verifies the drug type to prevent errors. Electromechanical dual overload protection prevents damage to the equipment and bottles. Photoelectric closed-loop counting and intelligent empty detection ensure accurate and continuous dispensing. This invention offers accurate and reliable dispensing, safe operation, and a high degree of intelligence.

The invention discloses a drug delivery carrier material, a drug delivery coating and a preparation method thereof, and belongs to the technical field of biological materials. The preparation method of the drug delivery coating comprises the following steps: reacting polypeptide containing hydrophobic amino acid with acidic protein in a solution system in which the pH value is 3-7, the temperature is 20-50 DEG C and an oxidizing agent exists, and mixing the prepared coating with a drug solution to realize drug filling, and then mixing with a phosphate solution to induce the micro-nano pore structure in the coating to be closed. The isoelectric points of the polypeptide and the acidic protein are both less than 7, and the difference value of the isoelectric points is less than or equal to 2.5; the acidic protein is negatively charged under the condition that the pH is greater than or equal to 7. According to the method, the contradiction that high loading and low burst release of an existing carrier are difficult to achieve at the same time is effectively solved, the coating is mild in drug loading condition, effective loading and activity retention of various active biomolecules can be achieved, the coating can also adapt to various drug types through modular design, and a brand new mode is provided for constructing a next-generation intelligent drug delivery system.

This specification discloses a method, device, and medium for drug supervision based on blockchain. The method includes: dividing a drug storage cabinet into multiple storage areas and collecting current environmental information and current drug information from each storage area; the current environmental information includes current temperature, current humidity, and current light conditions; determining whether the drug is stored in a pre-defined storage area based on the correspondence between drug type identifiers and multiple storage areas; if the drug is placed in a pre-defined storage area, storing the current environmental information corresponding to the storage area on the blockchain for monitoring the current environmental information within the storage area; issuing an early warning message; and determining the environmental control object to be adjusted based on the environmental information to be adjusted and the target storage area to be adjusted in the early warning message, and sending environmental control instructions to the environmental control object to be adjusted to achieve drug supervision.

The invention discloses a medicine filling dose automatic identification method and system, and relates to the technical field of production automation, a medicine main database and a main learning model are established, and the main learning model differentiates a corresponding derived learning model according to a medicine type; generating a lightweight learning model for each production batch of each type of medicine based on the derived learning model of each type of medicine, performing adaptive compensation adjustment on parameters of each level of the lightweight learning model based on difference information of the production batches, and fusing each level by outputting a corresponding dynamic fusion coefficient; and monitoring the operation process of each lightweight learning model based on a preset task trigger event, carrying out parameter retraining on the lightweight learning model by sharing the difference characteristic parameters, and carrying out canned dose real-time monitoring identification by using the retrained lightweight learning model. Automatic high-precision monitoring and intelligent optimization in the medicine production process are achieved, and the production efficiency and the product quality are improved.

The invention belongs to the technical field of drug detection, and provides a drug type identification method and system based on a sensor array. The method comprises the following steps: acquiring a response data time sequence of at least three sensors in a sensor array; extracting a steady-state feature and more than one dynamic feature corresponding to the sensor based on the response data time sequence of the sensor, the steady-state feature being a ratio of a response data average value in a tail data segment to a response data average value in an initial data segment in the response data time sequence; the more than one dynamic feature at least comprises a response data accumulated value of a predefined response region in the response data time sequence; and based on the steady-state features corresponding to the at least three sensors and the more than one dynamic feature, utilizing a classification model to obtain a drug type identification result of the to-be-detected article. The invention also provides a drug type identification system. The method is extremely low in cost, easy to operate, small in calculation amount and capable of rapidly and accurately achieving identification of various drugs.

The present invention provides a method for evaluating the neurotoxicity of environmental residues of quinolone antibiotics based on network toxicology. The present invention collects the types of quinolone drugs remaining in the environment and their drug targets, and collects neuropsychiatric diseases and their disease genes; establishes a drug target network, a neuropsychiatric disease module, and an overlapping network of drug target-disease modules, calculates network parameters to obtain key genes, uses the DAVID database to perform enrichment analysis on the key genes, and then performs network ranking analysis. Compared with traditional methods, the network toxicology of the present invention using big data high-throughput integration analysis technology has a wide coverage range of research objects, considers the toxicity of drugs from the perspectives of drug targets and disease genes, and provides a new idea for evaluating the risk size of adverse reactions of compounds.

A control system (1) for an infusion pump (2) comprises: a processor assembly (10); and a memory (11) storing instructions (12) that, when executed by the processor assembly (10), cause the processor assembly (10) to perform the steps of: receiving a dose of medicament and a type of medicament; reading a drug data structure (130) corresponding to the received drug type from a drug library (13), the drug library (13) adapted to store a plurality of drug data structures (130), each drug data structure (130) comprising a drug type entry (131) indicative of a drug type and a dose evolution entry (132) adapted to indicate whether a dose rate or a flow rate is to be kept constant over time; and determining an updated drug dose for the received drug type based on the dose evolution entry (132).

The utility model discloses a detector capable of detecting various drugs. The detector comprises a containing module and a detection module, wherein the detection module comprises a circular detection table, and the upper side of the circular detection table is fixedly connected with a circular boss. When the device is used, a urine sample of a suspected person is collected into the urine containing cylinder, the urine containing cylinder is placed into the cylinder, the clamping blocks are embedded into the clamping grooves, the urine containing cylinder is fixed, at the moment, the group of round holes correspond to the inclined grooves, the sleeve is moved upwards, the group of round holes are exposed, and the urine containing cylinder is fixed. Urine in the urine containing cylinder enters the inclined groove through the round hole and flows onto the detection test paper in the test paper containing groove, the urine reacts with the detection test paper, the detection test paper in the corresponding reaction is observed, name labels of drug types in the corresponding label containing groove are observed, and then a common drug rapid detection result can be obtained.

Anti-Aβ drug screening targets and a drug screening method. The screening method uses different Aβ aggregates prepared by simulating the in vivo environment and conditions as targets, to more accurately and effectively screen anti-Aβ candidate drugs; the Aβ targets involved in the screening method are aggregated and incubated under conditions approaching different in vivo microenvironments, and do not require labeling or modification; in addition, the screened drug molecules do not require any modification or labeling, and are not limited to any specific drug class, being widely applicable to different drug types, such as antibodies, peptides and small molecules, truly reflecting the interactions between targets and these drug molecules, greatly reducing the likelihood of problems such as false positives, false negatives or incorrect binding modes.

A prescription assistance device for assisting determination an appropriate prescription of drugs for treatment of heart failure from a variety of combinations of a type, an administration amount, and an administration timing of the drugs, which includes a processor configured to: acquire attribute data indicating an attribute of a heart failure patient, time series data on a state of the patient, including vital measurement values on the patient at a plurality of time points, including at least one of blood pressure, heart rate, urine volume and urine flow rate, and drug history data indicating a prescription history of a drug for the patient; generate, receive, or read, a plurality of prescription candidates in which combinations of a type, an administration amount, and an administration timing of the drug are different from one another; and predict an effect of the drug for each of a plurality of prescription candidates.

The application discloses a multi-channel drug fuzzy identification method and system based on surface-enhanced Raman spectroscopy. The method comprises the following steps: acquiring surface-enhanced Raman spectroscopy sequences of N sampling points of a sample to be measured; inputting the N surface-enhanced Raman spectroscopy sequences into N independent channel input ends of a trained CNN-GRU deep learning model respectively, and inputting the N surface-enhanced Raman spectroscopy sequences into a centralized channel input end of the trained CNN-GRU deep learning model; and the CNN-GRU deep learning model outputs a drug category or a drug category and a drug concentration grade. The CNN-GRU deep learning model introduces CNN and GRU into the analysis of Raman spectroscopy data features, so as to completely extract the detailed features and spatial correlation in the spectrum and be used for judging the drug type, can realize the rapid, simultaneous and high-accuracy identification of multiple drug types, and can also accurately quantitatively identify the concentration of the drug.

This invention discloses a medical real-time retail insight system under the O2O model, belonging to the field of intelligent supply chain management technology. It addresses the problem of information lag and inaccuracy caused by the system's inability to update inventory information in a timely manner when orders are generated or inventory changes. By combining real-time analysis of order impact metrics and dynamically adjusting the update frequency, it solves the problems of information lag and inaccuracy in traditional systems, ensuring that inventory information is synchronized with actual demand. Furthermore, considering the special characteristics of drug storage environments and drug types, it quantifies drug complexity and uses the TOPSIS algorithm to differentiate update methods, reducing the risk of drug expiration due to improper storage conditions or untimely updates, thus ensuring medical safety.

1. The name of the design product: bird repeller (mirror medicine type). 2. The use of the design product: the design product is used as a bird repeller, installed on power equipment to drive birds. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: front view.

The invention discloses a multi-target drug collaborative design system and method, and relates to the technical field of medicine processing, a data acquisition module is used for acquiring drug related data, a data processing module is used for processing and integrating the drug related data, and a drug effect score is calculated to obtain a drug collaborative effect score; then inputting the drug types into a pre-established side effect prediction model and a pre-established toxicity prediction model through a data analysis module, outputting to obtain a side effect generation probability and a toxicity prediction probability, carrying out candidate calculation on the side effect generation probability and the toxicity prediction probability and a drug synergistic effect score, and outputting to obtain a candidate compound generation coefficient; and the drug design module performs optimal multi-target drug scheme design based on the candidate compound generation coefficient when the size of the candidate compound generation coefficient is greater than a threshold value, so that a function of performing multi-target drug synergistic effect quantification and prediction based on various multi-source data can be realized.

A platform configured to predict type or family of an unknown drug candidate compound, the platform including: a living cell or a tissue; a detector that measures an indicator of a cellular response by the living cell or tissue upon exposure to the unknown drug candidate compound; a memory configured to store data related to the indicator of the cellular response detected by the detector from a library of drug types and / or families; and one or more processing unit(s) configured to: process the data related to the indicator of the cellular response of the living cell or tissue upon exposure to the unknown drug candidate compound, and compare cellular response data from the library of drug types and / or families, so that a drug type and / or a drug family and / or a mechanism of action of the unknown drug candidate compound can be predicted on the basis of a similarity between the detected cellular response data of the unknown drug candidate compound and the cellular response data of the library of drug types and / or families. Also disclosed are methods of screening an unknown drug, including: comparing the data measured from a test cell to corresponding cellular response data in a library of known drug types, and determining a relationship between the unknown drug and a known drug type or a known drug family to predict the type or family of the unknown drug.